research
∙
07/10/2023
Automatic Debiased Machine Learning for Covariate Shifts
In this paper we address the problem of bias in machine learning of para...
share
research
∙
07/20/2022
Efficient Bias Correction for Cross-section and Panel Data
Bias correction can often improve the finite sample performance of estim...
research
∙
10/06/2021
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
Many causal and policy effects of interest are defined by linear functio...
research
∙
05/31/2021
A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees
Debiased machine learning is a meta algorithm based on bias correction a...
research
∙
04/30/2021
Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression
We give debiased machine learners of parameters of interest that depend ...
research
∙
09/04/2020
Heterogeneous Coefficients, Control Variables, and Identification of Treatment Effects
Multidimensional heterogeneity and endogeneity are important features of...
research
∙
12/27/2019
Minimax Semiparametric Learning With Approximate Sparsity
Many objects of interest can be expressed as a linear, mean square conti...
research
∙
11/24/2018
Heterogenous Coefficients, Discrete Instruments, and Identification of Treatment Effects
Multidimensional heterogeneity and endogeneity are important features of...
research
∙
09/14/2018
Learning L2 Continuous Regression Functionals via Regularized Riesz Representers
Many objects of interest can be expressed as an L2 continuous functional...
research
∙
01/27/2018
